{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,4]],"date-time":"2024-08-04T00:23:42Z","timestamp":1722731022914},"reference-count":22,"publisher":"Institute of Electronics, Information and Communications Engineers (IEICE)","issue":"8","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Inf. &amp; Syst."],"published-print":{"date-parts":[[2024,8,1]]},"DOI":"10.1587\/transinf.2023edp7212","type":"journal-article","created":{"date-parts":[[2024,7,31]],"date-time":"2024-07-31T22:17:22Z","timestamp":1722464242000},"page":"1059-1069","source":"Crossref","is-referenced-by-count":0,"title":["FSAMT: Face Shape Adaptive Makeup Transfer"],"prefix":"10.1587","volume":"E107.D","author":[{"given":"Haoran","family":"LUO","sequence":"first","affiliation":[{"name":"Graduate School of Creative Science and Engineering, Waseda University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tengfei","family":"SHAO","sequence":"additional","affiliation":[{"name":"Graduate School of Creative Science and Engineering, Waseda University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenglei","family":"LI","sequence":"additional","affiliation":[{"name":"Graduate School of Creative Science and Engineering, Waseda University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Reiko","family":"HISHIYAMA","sequence":"additional","affiliation":[{"name":"Graduate School of Creative Science and Engineering, Waseda University"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"532","reference":[{"key":"1","unstructured":"[1] Fortune Business Insights. Makeup market size, share &amp; covid-19 impact analysis, 2023."},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] H. Deng, C. Han, H. Cai, G. Han, and S. He, \u201cSpatially-invariant style-codes controlled makeup transfer,\u201d <i>Proc. IEEE\/CVF Conf. Computer Vision and Pattern Recognition<\/i>, pp.6549-6557, 2021.","DOI":"10.1109\/CVPR46437.2021.00648"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] T. Li, R. Qian, C. Dong, S. Liu, Q. Yan, W. Zhu, and L. Lin, \u201cBeautygan: Instance-level facial makeup transfer with deep generative adversarial network,\u201d <i>Proc. 26th ACM Int. Conf. Multimedia<\/i>, pp.645-653, 2018. 10.1145\/3240508.3240618","DOI":"10.1145\/3240508.3240618"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] H.-J. Chen, K.-M. Hui, S.-Y. Wang, L.-W. Tsao, H.-H. Shuai, and W.-H. Cheng, \u201cBeautyglow: On-demand makeup transfer framework with reversible generative network,\u201d <i>Proc. IEEE\/CVF Conf. Computer Vision and Pattern Recognition<\/i>, pp.10042-10050, 2019.","DOI":"10.1109\/CVPR.2019.01028"},{"key":"5","doi-asserted-by":"crossref","unstructured":"[5] W. Jiang, S. Liu, C. Gao, J. Cao, R. He, J. Feng, and S. Yan, \u201cPsgan: Pose and expression robust spatial-aware gan for customizable makeup transfer,\u201d <i>Proc. IEEE\/CVF Conf. Computer Vision and Pattern Recognition<\/i>, pp.5194-5202, 2020.","DOI":"10.1109\/CVPR42600.2020.00524"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] R. Kips, P. Gori, M. Perrot, and I. Bloch, \u201cCa-gan: Weakly supervised color aware gan for controllable makeup transfer,\u201d <i>Computer Vision-ECCV 2020 Workshops: Glasgow, UK, August 23-28, 2020, Proceedings, Part III 16<\/i>, pp.280-296, Springer, 2020. 10.1007\/978-3-030-67070-2_17","DOI":"10.1007\/978-3-030-67070-2_17"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] J. Xiang, J. Chen, W. Liu, X. Hou, and L. Shen, \u201cRamgan: Region attentive morphing gan for region-level makeup transfer,\u201d <i>Computer Vision-ECCV 2022: 17th European Conference, Tel Aviv, Israel, Oct. 23-27, 2022, Proceedings, Part XXII<\/i>, pp.719-735, Springer, 2022. 10.1007\/978-3-031-20047-2_41","DOI":"10.1007\/978-3-031-20047-2_41"},{"key":"8","doi-asserted-by":"publisher","unstructured":"[8] X. Yang, T. Taketomi, and Y. Kanamori, \u201cMakeup extraction of 3d representation via illumination-aware image decomposition,\u201d <i>Computer Graphics Forum<\/i>, vol.42, no.2, pp.293-307, Wiley Online Library, 2023. 10.1111\/cgf.14762","DOI":"10.1111\/cgf.14762"},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] C. Yang, W. He, Y. Xu, and Y. Gao, \u201cElegant: Exquisite and locally editable gan for makeup transfer,\u201d <i>European Conf. Computer Vision<\/i>, pp.737-754, Springer, 2022. 10.1007\/978-3-031-19787-1_42","DOI":"10.1007\/978-3-031-19787-1_42"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] Q. Gu, G. Wang, M.T. Chiu, Y.-W. Tai, and C.-K. Tang, \u201cLadn: Local adversarial disentangling network for facial makeup and de-makeup,\u201d <i>Proc. IEEE\/CVF Int. Conf. Computer Vision<\/i>, pp.10481-10490, 2019.","DOI":"10.1109\/ICCV.2019.01058"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] T. Nguyen, A.T. Tran, and M. Hoai, \u201cLipstick ain&apos;t enough: beyond color matching for in-the-wild makeup transfer,\u201d <i>Proc. IEEE\/CVF Conf. Computer Vision and Pattern Recognition<\/i>, pp.13305-13314, 2021.","DOI":"10.1109\/CVPR46437.2021.01310"},{"key":"12","doi-asserted-by":"crossref","unstructured":"[12] K. He, X. Zhang, S. Ren, and J. Sun, \u201cDeep residual learning for image recognition,\u201d <i>Proc. IEEE Conf. computer vision and pattern recognition<\/i>, pp.770-778, 2016.","DOI":"10.1109\/CVPR.2016.90"},{"key":"13","unstructured":"[13] A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A.N. Gomez, \u0141. Kaiser, and I. Polosukhin, \u201cAttention is all you need,\u201d <i>Advances in neural information processing systems<\/i>, 30, 2017."},{"key":"14","doi-asserted-by":"publisher","unstructured":"[14] J. Shen, Y. Qu, W. Zhang, and Y. Yu, \u201cWasserstein distance guided representation learning for domain adaptation,\u201d <i>Proc. AAAI Conf. Artificial Intelligence<\/i>, vol.32, no.1, 2018. 10.1609\/aaai.v32i1.11784","DOI":"10.1609\/aaai.v32i1.11784"},{"key":"15","unstructured":"[15] Z. Wei, \u201cOriental eye shape classification and cosmetology,\u201d <i>Medical Aesthetics and Cosmetology<\/i>, (5):38-39, 1 1995."},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] G. Huang, Z. Liu, L. Van Der Maaten, and K.Q. Weinberger, \u201cDensely connected convolutional networks,\u201d <i>Proc. IEEE Conf. computer vision and pattern recognition<\/i>, pp.4700-4708, 2017.","DOI":"10.1109\/CVPR.2017.243"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] Y. Feng, F. Wu, X. Shao, Y. Wang, and X. Zhou, \u201cJoint 3d face reconstruction and dense alignment with position map regression network,\u201d <i>Proc. European Conf. computer vision (ECCV)<\/i>, pp.534-551, 2018.","DOI":"10.1007\/978-3-030-01264-9_33"},{"key":"18","doi-asserted-by":"crossref","unstructured":"[18] Z. Liu, P. Luo, X. Wang, and X. Tang, \u201cDeep learning face attributes in the wild,\u201d <i>Proc. IEEE Int. Conf. computer vision<\/i>, pp.3730-3738, 2015.","DOI":"10.1109\/ICCV.2015.425"},{"key":"19","unstructured":"[19] S. Yaorui and B. Fanliang, \u201cEye type classification based on convolutional neural network and semantic features,\u201d <i>Electronic Measurement Technology<\/i>, 42(3):16-20, 1 2019."},{"key":"20","unstructured":"[20] S. Jinguang and R. Wenzhao, \u201cCurve similarity eye type classification\u201d <i>Computer Science and Exploration<\/i>, 11(8):1305-1313, 1 2017."},{"key":"21","unstructured":"[21] K. Simonyan and A. Zisserman, \u201cVery deep convolutional networks for large-scale image recognition,\u201d <i>arXiv preprint arXiv:1409.1556<\/i>, 2014."},{"key":"22","doi-asserted-by":"crossref","unstructured":"[23] T.-Y. Lin, P. Doll\u00e1r, R. Girshick, K. He, B. Hariharan, and S. Belongie, \u201cFeature pyramid networks for object detection,\u201d <i>Proc. IEEE Conf. computer vision and pattern recognition<\/i>, pp.2117-2125, 2017.","DOI":"10.1109\/CVPR.2017.106"}],"container-title":["IEICE Transactions on Information and Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E107.D\/8\/E107.D_2023EDP7212\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,3]],"date-time":"2024-08-03T04:18:04Z","timestamp":1722658684000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transinf\/E107.D\/8\/E107.D_2023EDP7212\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,1]]},"references-count":22,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2024]]}},"URL":"https:\/\/doi.org\/10.1587\/transinf.2023edp7212","relation":{},"ISSN":["0916-8532","1745-1361"],"issn-type":[{"type":"print","value":"0916-8532"},{"type":"electronic","value":"1745-1361"}],"subject":[],"published":{"date-parts":[[2024,8,1]]},"article-number":"2023EDP7212"}}